Gene Therapy for Hereditary Hematological Disorders: From Clinical Breakthroughs to Future Horizons

By: Zhenghe Chen · Wei Wang · Youlan Wu · Yawen Qiang · Weisheng Cheng · Fang Liu

27 August 2026 at 08:00

Gene therapy is transforming hereditary hematological disorders. This review summarizes approved gene addition, editing, and silencing strategies for sickle cell disease, thalassemia, and hemophilia, highlights curative potential, and discusses remaining challenges such as immune responses, cost, and accessibility

Synchronized latency reversal and immune clearance by a multifunctional fusion protein enables HIV-1 reservoir reduction

Molecular Therapy

By: Fei Luo · Yinghui Cao · Na Liu · Xiaowei Wang · Wenjia Hu · Yajun Yan · Xiaoqing Xie · Dongmei Jiang · Yangyang Xu · Jun Wang · Hongyu Wang · Zhenyang Yu · Peng Qian · Shicheng Wan · Zhida Liu · Yan Gong · Tielong Chen · Shihui Song · Yong Xiong · Xiaoping Tang · Linghua Li · Li Wang · Haisheng Yu · Liang Cheng

29 July 2026 at 08:00

Latent HIV reservoirs evade both antiviral therapy and immune surveillance. Luo and colleagues develop a multifunctional fusion protein that couples reservoir reactivation with targeted immune engagement and clearance, offering a coordinated strategy to expose and eliminate persistent HIV-infected cells.

Beyond One-Size-Fits-All: Sample-Adaptive Strategy Routing for Vision Token Pruning in MLLMs

cs.AI, q-bio.NC updates on arXiv.org

By: Haiji Liang · Pengfei Zhou · Zhenglin Wan · Wei Wang · Yang You · Wangbo Zhao

10 September 2026 at 12:00

arXiv:2609.10346v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) process hundreds or thousands of visual tokens per image, incurring prohibitive inference costs. While existing vision token pruning methods mitigate this overhead, they implicitly assume that a single fixed pruning strategy can be applied uniformly across all inputs. Our analysis further reveals that ranking pruning methods by average benchmark accuracy conceals substantial sample-wise complementarity: although the average-best strategy excels overall, alternative strategies prove superior on a significant fraction of individual samples. To harness this diversity, we propose VIP-Router, a lightweight VIsion Pruning Router that adaptively selects the pruning strategy predicted to be best suited to each input at a specified pruning level. Conditioned on low-cost visual and textual features, VIP-Router identifies the most suitable candidate strategy while retaining full-token inference as an option when pruning is predicted to be unfavorable. Evaluated on a curated suite of pruning-sensitive visual perception benchmarks, VTC-Bench Group A, VIP-Router consistently outperforms the best fixed strategy baseline across all reduction ratios, achieving a 26.9% relative improvement in average accuracy, and a 22.0% relative increase in average utility after accounting for realized token cost. Crucially, VIP-Router operates in a plug-and-play manner without modifying underlying pruning algorithms or model weights, introducing trainable parameters equivalent to merely 0.017\% of the backbone. Furthermore, VIP-Router proves effective across various MLLM backbones and yields consistent gains on unseen benchmarks, highlighting the potential of sample adaptive routing for visual token pruning.

Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning

cs.AI, q-bio.NC updates on arXiv.org

By: Zikang Shan · Han Zhong · Liwei Wang · Li Zhao

10 September 2026 at 12:00

arXiv:2604.10701v2 Announce Type: replace-cross Abstract: Credit assignment is a central challenge in reinforcement learning (RL). Classical actor-critic methods address this challenge through fine-grained advantage estimation based on a learned value function. However, learned value models are often avoided in modern large language model (LLM) RL because conventional discriminative critics are difficult to train reliably. We revisit value modeling and argue that this difficulty is partly due to limited expressiveness. In particular, representation complexity theory suggests that value functions can be hard to approximate under the one-shot prediction paradigm used by existing value models, and our scaling experiments show that such critics do not improve reliably with scale. Motivated by this observation, we propose Generative Actor-Critic (GenAC), which replaces one-shot scalar value prediction with a generative critic that performs chain-of-thought reasoning before producing a value estimate. We further introduce In-Context Conditioning, which helps the critic remain calibrated to the current actor throughout training. GenAC improves value approximation, ranking reliability, and out-of-distribution generalization, and these gains translate into stronger downstream RL performance than both value-based and value-free baselines. Overall, our results suggest that stronger value modeling is a promising direction for improving credit assignment in LLM reinforcement learning.

CAFs shape the immunosuppressive microenvironment of pancreatic cancer through the Lin28b-STING Axis

(Multiomics OR Omics) AND (Pancreatic)

By: Minghe Fan · Ziyang Zhang · Wei Xu · Yuwei Liu · Ruoxuan Wang · Wenqin Hao · Sihan Huo · Yuyao Guo · Lina Wang · Ang Lv · Xiangyu Liu · Wei Wang · Chuanhui Han · Ying Zhao

3 September 2026 at 18:00

Nat Commun. 2026 Aug 7;17(1):9491. doi: 10.1038/s41467-026-76495-3.

ABSTRACT

Cancer-associated fibroblasts comprise diverse functionally distinct cellular subsets, with certain subpopulations exerting pivotal influence in shaping the pancreatic cancer immune microenvironment. Here we show that Lin28b⁺ cancer-associated fibroblasts contribute to establishing an immunologically cold tumor microenvironment in pancreatic ductal adenocarcinoma. Mechanistically, Lin28b directly binds to STING mRNA and promotes its degradation, thereby suppressing STING expression and downstream type I interferon signaling. Loss of Lin28b in cancer-associated fibroblasts activates the cGAS-STING-interferon signaling cascade, enhancing dendritic cell antigen presentation and CD8⁺ T cell cytotoxic function. Importantly, genetic inhibition of Lin28b in cancer-associated fibroblasts enhances sensitivity to anti-PD-L1 immune checkpoint blockade therapy. These findings reveal that targeting the Lin28b-STING axis represents a promising therapeutic strategy for overcoming the intrinsic resistance of pancreatic ductal adenocarcinoma to immunotherapy.

PMID:42693143 | PMC:PMC13542369 | DOI:10.1038/s41467-026-76495-3

Engineered genomic attachment sites for site-specific recombinases enable high-efficiency integration in plants and human cells

Nature Biotechnology - Issue - nature.com science feeds

By: Linlin Yan · Lingyu Zhou · Qiang Gao · Lijuan Li · Lina Guo · Yidong Ran · Lixiao Zhang · Kang Zhang · Zhiwei Wang · Yan Li · Shengnan Li · Kevin Tianmeng Zhao

2 September 2026 at 08:00

Nature Biotechnology, Published online: 02 September 2026; doi:10.1038/s41587-026-03294-y

DNA recombination in rice is optimized by engineering genomic attachment sites.

Artificial intelligence-assisted early screening of lung cancer and accurate diagnosis of pulmonary nodules: research progress and clinical prospects from radiomics to multi-omics integration: a narrative review

Pulmonary nodule

By: Junwei Wang · Guixue Yang · Zhuoxin Dai · Miao Yang · Peng Dai · Mengyang Chang · Quan-Xing Liu · Ji-Gang Dai

17 June 2026 at 18:00

J Thorac Dis. 2026 May 31;18(5):537. doi: 10.21037/jtd-2026-1-0315. Epub 2026 Apr 30.

ABSTRACT

BACKGROUND AND OBJECTIVE: Lung cancer remains one of the leading causes of cancer-related death worldwide. Although low-dose computed tomography (LDCT) has improved early detection, false-positive results, overdiagnosis, and interobserver variability continue to limit screening efficiency and downstream management of pulmonary nodules. This narrative review summarizes recent progress in artificial intelligence (AI)-assisted screening, radiomics-based nodule characterization, and multi-omics integration for the precision diagnosis of lung cancer.

METHODS: A narrative review with thematic analysis was conducted using representative literature on AI-assisted lung cancer screening, quantitative imaging analysis of pulmonary nodules, radiogenomic and multi-omics integration, and clinical translation challenges. Studies were synthesized to highlight technical advances, diagnostic performance, strengths, limitations, and barriers to implementation.

KEY CONTENT AND FINDINGS: AI improves nodule detection, second-reader support, workflow efficiency, and malignancy-risk estimation in LDCT screening. Radiomics converts CT images into quantitative features that can improve discrimination between benign and malignant nodules, especially when combined with clinical variables or deep-learning models. Beyond imaging alone, radiogenomic and other multi-omics approaches link imaging phenotypes with molecular alterations, treatment response, and prognosis, thereby supporting more individualized management. However, current evidence remains limited by dataset heterogeneity, retrospective design, limited interpretability, and insufficient multicenter prospective validation.

CONCLUSIONS: AI-based imaging and multi-omics integration offer a promising pathway toward earlier detection and more precise diagnosis of lung cancer. Broader clinical adoption will depend on standardized data acquisition, robust external validation, interpretable models, and careful governance of privacy, ethics, and workflow integration.

PMID:42306713 | PMC:PMC13266817 | DOI:10.21037/jtd-2026-1-0315

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